There are a number of consistencies and symmetries inherent to the motion forecasting problem. Recently, there has been an effort to integrate this information, either as an explicit loss or in the model structure to improve the model performance. Certain symmetries such as rotation, translation and scale symmetries have been incorporated into the loss function using data augmentation in various methods [1]}, [2]}, [3]}. [4]} incorporates rotation and translation invariance explicitly into the model structure and input representation. [2]} enforces temporal and spatial consistency constraints into the loss function, which is shown to improve prediction accuracy.
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